How do mate-finding Allee effects affect evolutionary rescue?
Bibliographic record
Abstract
In sexually reproducing populations, the challenge of finding mates at low densities can impose a strong demographic Allee effect. Environmental change can cause a population to fall below its Allee threshold by reducing the population size or by increasing the threshold if the latter depends on affected life-history traits. Evolutionary rescue then relies on overcoming the Allee effect, which gets increasingly difficult as the population declines. Despite mate-finding Allee effects being common, most models of evolutionary rescue assume that mating is assured even at low densities. Here, we set up a population genetic model to study the potential for evolutionary rescue of a population below its Allee threshold. For the analysis, we combine stochastic computer simulations with mathematical arguments. As expected, mate limitation can severely impede rescue, but the extent differs across sexual systems. We further show that it shifts the optimal sex ratio for dioecious but not for androdioecious populations, alters optimal evolutionary routes when there are trade-offs between increasing mate-finding efficiency and fecundity, and enhances the importance of standing genetic variation relative to de novo mutants. Overall, our results highlight the importance of accounting for positive density dependence in the assessment of a population's scope for evolutionary rescue.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".